生产级LLM的平台工程实战手册

AI 摘要

文章介绍了一个大型零售企业构建的生产级LLM平台工程实践。团队通过将LLM栈视为平台基础设施而非应用层问题，在不更换基础模型的情况下，将幻觉率从15%降至1.5%。核心手段包括自动重试循环、意图验证门、提示词注册表、工具授权默认拒绝策略，以及在请求入口处对幻觉率和token成本进行可观测性埋点。

正文节选

Key Takeaways - Hallucination rate is a platform-controllable metric. By wrapping the model in an automated retry loop that catches formatting, grounding, and infrastructure errors on the fly, we cut production hallucination rate from fifteen percent to 1.5 percent without touching the foundation model. - An intent-validation gate that returns "unclassified" instead of defaulting to the highest-scoring agent eliminates off-intent hallucinations and improves multi-agent routing. - By storing prom

发布时间：2026-10-05 19:00

抓取时间：2026-10-05 19:27

来源机构：InfoQ